Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia.

Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms.

These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.

Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia.

Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms.

These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.

TRUST-BASED COMMUNICATION SYSTEMS FOR INTERNET OF THINGS APPLICATIONS

Highlighting the challenges and difficulties in implementing trust-based communication systems for Internet of Things (IoT) services and applications, this innovative new volume is a critical reference source for academics, professionals, engineers, technology designers, analysts, and students.

The primary objective of this edited book is to deliver technologies to improve trust and eliminate malicious actors in participatory exchanges throughout communication using Internet of Things (IOT) devices such that these methods should not only be able to identify bad actors but also to improve communication and trust in the environment without violating object privacy. Whether as a reference for the engineer or scientist or a textbook for the student, this is a must-have for any library.

TRUST-BASED COMMUNICATION SYSTEMS FOR INTERNET OF THINGS APPLICATIONS

Highlighting the challenges and difficulties in implementing trust-based communication systems for Internet of Things (IoT) services and applications, this innovative new volume is a critical reference source for academics, professionals, engineers, technology designers, analysts, and students.

The primary objective of this edited book is to deliver technologies to improve trust and eliminate malicious actors in participatory exchanges throughout communication using Internet of Things (IOT) devices such that these methods should not only be able to identify bad actors but also to improve communication and trust in the environment without violating object privacy. Whether as a reference for the engineer or scientist or a textbook for the student, this is a must-have for any library.

Knowledge Engineering (KE) is a field within artificial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specific domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent findings and innovative research in the information system and KE domain. Highlighting the challenges and difficulties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will find this book a valuable source of ideas and guidance.

Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia.

Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms.

These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.

Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia.

Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms.

These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.

TRUST-BASED COMMUNICATION SYSTEMS FOR INTERNET OF THINGS APPLICATIONS

Highlighting the challenges and difficulties in implementing trust-based communication systems for Internet of Things (IoT) services and applications, this innovative new volume is a critical reference source for academics, professionals, engineers, technology designers, analysts, and students.

The primary objective of this edited book is to deliver technologies to improve trust and eliminate malicious actors in participatory exchanges throughout communication using Internet of Things (IOT) devices such that these methods should not only be able to identify bad actors but also to improve communication and trust in the environment without violating object privacy. Whether as a reference for the engineer or scientist or a textbook for the student, this is a must-have for any library.

TRUST-BASED COMMUNICATION SYSTEMS FOR INTERNET OF THINGS APPLICATIONS

Highlighting the challenges and difficulties in implementing trust-based communication systems for Internet of Things (IoT) services and applications, this innovative new volume is a critical reference source for academics, professionals, engineers, technology designers, analysts, and students.

The primary objective of this edited book is to deliver technologies to improve trust and eliminate malicious actors in participatory exchanges throughout communication using Internet of Things (IOT) devices such that these methods should not only be able to identify bad actors but also to improve communication and trust in the environment without violating object privacy. Whether as a reference for the engineer or scientist or a textbook for the student, this is a must-have for any library.

Knowledge Engineering (KE) is a field within artificial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specific domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent findings and innovative research in the information system and KE domain. Highlighting the challenges and difficulties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will find this book a valuable source of ideas and guidance.